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Complete identification methods for the causal hierarchy

Complete identification methods for the causal hierarchy
Complete identification methods for the causal hierarchy
We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from a joint distribution over the observable variables; cause-effect relationships, derived from distributions resulting from external interventions; and counterfactuals, derived from distributions that span multiple "parallel worlds" and resulting from simultaneous, possibly conflicting observations and interventions. We completely characterize cases where a given causal query can be computed from information lower in the hierarchy, and provide algorithms that accomplish this computation. Specifically, we show when effects of interventions can be computed from observational studies, and when probabilities of counterfactuals can be computed from experimental studies. We also provide a graphical characterization of those queries which cannot be computed (by any method) from queries at a lower layer of the hierarchy.
1941-1979
Shpitser, Ilya
4d295b9b-39e8-417f-b38d-fbb5d7df6992
Pearl, Judea
d4317e37-9d5f-4fdc-84ad-c7bf98f99476
Shpitser, Ilya
4d295b9b-39e8-417f-b38d-fbb5d7df6992
Pearl, Judea
d4317e37-9d5f-4fdc-84ad-c7bf98f99476

Shpitser, Ilya and Pearl, Judea (2008) Complete identification methods for the causal hierarchy. Journal of Machine Learning Research, 9, 1941-1979.

Record type: Article

Abstract

We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from a joint distribution over the observable variables; cause-effect relationships, derived from distributions resulting from external interventions; and counterfactuals, derived from distributions that span multiple "parallel worlds" and resulting from simultaneous, possibly conflicting observations and interventions. We completely characterize cases where a given causal query can be computed from information lower in the hierarchy, and provide algorithms that accomplish this computation. Specifically, we show when effects of interventions can be computed from observational studies, and when probabilities of counterfactuals can be computed from experimental studies. We also provide a graphical characterization of those queries which cannot be computed (by any method) from queries at a lower layer of the hierarchy.

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Published date: September 2008
Organisations: Statistics

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Local EPrints ID: 350584
URI: http://eprints.soton.ac.uk/id/eprint/350584
PURE UUID: 18df2d7c-7c5b-4bbd-b342-9f40c3e53991

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Date deposited: 08 Apr 2013 10:51
Last modified: 14 Mar 2024 13:29

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Contributors

Author: Ilya Shpitser
Author: Judea Pearl

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